1. Field of the Invention
The present invention relates generally to computed tomographic (CT) imaging apparatus that performs three-dimensional (3D) image reconstruction by processing cone beam measurement data representative of an object, and more specifically, to a fast and efficient multiprocessor arrangement for performing the image reconstruction processing.
2. Description of the Background Art
Recently a system employing cone beam geometry has been developed for three-dimensional (3D) computed tomographic (CT) imaging that includes a cone beam x-ray source and a 2D area detector. An object to be imaged is scanned, preferably over a 360.degree. angular range and along its entire length, by any one of various methods wherein the position of the area detector is fixed relative to the source, and relative rotational and translational movement between the source and object provides the scanning (irradiation of the object by radiation energy). The cone beam approach for 3D CT has the potential to achieve 3D imaging in both medical and industrial applications with improved speed, as well as improved dose utilization when compared with conventional 3D CT apparatus (i.e., a stack of slices approach obtained using parallel or fan beam x-rays).
As a result of the relative movement of the cone beam source to a plurality of source positions (i.e., "views") along the scan path, the detector acquires a corresponding plurality of sets of cone beam projected measurement data (referred to hereinafter as measurement data), each set of measurement data being representative of x-ray attenuation caused by the object at a respective one of the source positions. After completion of measurement data acquisition, the measurement data is processed for reconstructing a 3D image of the object.
As compared with the processing required for reconstructing an image when using an x-ray source supplying parallel or fan beams, the processing of the measurement data acquired when using a cone beam source is computationally much more complex. This is because when using a parallel or fan beam source, the measurement data is already directly representative of a 2D Radon transform of a cross-section of the object. However, this is not the case when using a cone beam source, and complex processing of the acquired measurement data is required to develop appropriate Radon transform data. Such processing for exactly reconstructing an image of the object typically, comprises:
1) conversion of the measurement data to Radon derivative data. This may be accomplished using the techniques described in U.S. Pat. No. 5,257,183 entitled METHOD AND APPARATUS FOR CONVERTING CONE BEAM X-RAY PROJECTION DATA TO PLANAR INTEGRAL AND RECONSTRUCTING A THREE-DIMENSIONAL COMPUTERIZED TOMOGRAPHY (CT) IMAGE OF AN OBJECT issued Oct. 26, 1993, hereby incorporated by reference,
2) conversion of the Radon derivative data to Radon data at polar grid points using, for example, the technique described in U.S. Pat. No. 5,446,776 entitled TOMOGRAPHY WITH GENERATION OF RADON DATA ON POLAR GRID POINTS issued Aug. 8, 1995, also hereby incorporated by reference, and
3) performing an inverse 3D Radon transformation of the Radon data using known techniques, such as those described in detail in the forenoted U.S. Pat. No. 5,257,183 for reconstructing image data that, when applied to a display, provides a view of the 3D CT image of the object.
Although the theory for exactly reconstructing an image using cone beam measurement data is generally known, such as from the US patents noted above, a practical implementation of the processing turns out to be quite problematic. Not only is the amount of measurement data to be processed very large and rapidly acquired in accordance with a timing that is mainly determined by the geometry of the scan path, but, as noted above, the calculations required on the acquired data are quite complex. The most computationally expensive part of the object reconstruction is the calculation of the Radon derivative data (steps 1 and 2 noted above). As noted in the above US patents, as well as in detail in U.S. Pat. No. 5,463,666 entitled HELICAL AND CIRCLE SCAN REGION OF INTEREST COMPUTERIZED TOMOGRAPHY issued Oct. 31, 1995, hereby incorporated by reference, for calculating the value of the Radon data at a given Radon sample point, it is typically necessary to process the measurement data acquired from several source positions, with the measurement data from each source position developing a contribution to the final value for that sample point by way of data combination. Typically one needs to calculate about 100.times.10.sup.6 line integral derivatives during object reconstruction. Since each line integral derivative requires the calculation of two single line integrals (because one uses the difference between two closely spaced line integrals to calculate a single line integral derivative) 200.times.10.sup.6 single line integral calculations are required. However, before one can even begin to perform these line integral derivative calculations, one has to compute for each Radon sample which source positions will provide the measurement data that must be processed, and determine the extent of the lines on the measurement data along which the integration must be performed. In order to compute the contributing source positions, one has to intersect the source scanning path with the Radon integration plane as explained in the forenoted U.S. Pat. No. 5,463,666. When using a spiral scan path, this requires the solution of transcendental equations, which are computationally expensive. The complexity of these above-noted calculations leads to severe bottlenecks in processing of the measurement data, so as to prevent rapid and efficient image reconstruction.
U.S. patent application Ser. No. 08/940,489, entitled A REDUCTION OF HITLIST SIZE IN SPIRAL CONE BEAM CT BY USE OF LOCAL RADON ORIGINS, filed Sep. 30, 1997, incorporated herein by reference, describes a rapid and efficient technique for processing the acquired measurement data to develop the Radon derivative data. A spherical coordinate system (r, .theta., .phi.) defining a Radon space partitioned by a plurality of vertically oriented co-axial .phi.-planes is used to facilitate a subsequent inversion processing of the Radon data. Instead of performing all of the conversion calculations "on the fly", this new technique makes use of a pre-calculated "relative hitlist" for speeding up the conversion.
Briefly, the relative hitlist comprises a memory of pre-calculated image reconstruction processing information which is used to greatly aid the conversion of the measurement data to Radon data. The hitlist information is determined primarily by geometric parameters of the imaging apparatus, and are therefor already determined before imaging operation of the apparatus. Such parameters are the pitch and other characteristics of the source/detector scan path, the dimensions of the object, the detector resolution, and the sampling of the scan path and the Radon space. These parameters define the line integrals which need to be calculated in the measurement data to develop the desired samples of the Radon data. Thus, the hitlist information indicates the correspondence between points in Radon space and the source positions that contribute thereto, parameters that define the line integrals that need to be calculated in the measurement data acquired at each of the source positions, as well as other information useful for image reconstruction processing. Typically the imaging system manufacturer will pre-calculate the hitlist information and store it in a memory. The hitlist information is used during run-time (imaging) operation of the apparatus to assist the conversion processing of the acquired measurement data into the many samples of Radon derivative data needed to fill up the region of Radon support for proper reconstruction of the object. Furthermore, due to a symmetry that is induced in the subsequent Radon inversion processing, the information that is stored for only one of the Radon space .phi.-planes can be used for calculating Radon derivative data for all of the other Radon space .phi.-planes. Accordingly, the memory requirements for the hitlist are greatly reduced. Use of the pre-calculated hitlist results in a great improvement in the speed and efficiency of the image reconstruction processing as compared to conversion processing without use of a hitlist.
It would be desirable to provide an efficient multi-processor arrangement for carrying out image reconstruction processing which uses such pre-calculated information.